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Principal Data Scientist Generative Ai Jobs (NOW HIRING)

... AI assistants with the context needed to drive conversational shopping. What You Will Do As the Principal Data Scientist , you will drive the generative and context-aware evolution of our platform.

As a Senior Data Scientist, Generative AI & Agentic Systems, you will help drive the bank's AI transformation by designing, developing, and deploying Large Language Model (LLM) solutions, Retrieval ...

As a Senior Data Scientist, Generative AI & Agentic Systems, you will help drive the bank's AI transformation by designing, developing, and deploying Large Language Model (LLM) solutions, Retrieval ...

We are seeking a Principal Data Scientist specializing in Generative AI to serve as our top technical authority on model science, advanced retrieval methodologies, AI quality, and evaluation strategy.

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How much do principal data scientist generative ai jobs pay per year?

As of Sep 5, 2026, the average yearly pay for principal data scientist generative ai in the United States is $144,009.00, according to ZipRecruiter salary data. Most workers in this role earn between $117,500.00 and $169,000.00 per year, depending on experience, location, and employer.
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Infographic showing various Principal Data Scientist Generative Ai job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 85% Full Time, 11% Part Time, and 3% Contract. Highlights an 86% Physical, 3% Hybrid, and 11% Remote job distribution, with an average salary of $144,009 per year, or $69.2 per hour.

Data Scientist / Generative AI Specialist

Resource Innovative Technologies LLC

Charlotte, NC • On-site

Other

Posted 8 days ago


Job description

Job Description: Data Scientist / Generative AI Specialist

Location: Charolette, NC

Position Overview

We are seeking a highly motivated and results-driven Data Scientist with Generative AI expertise to join our growing team. This role requires a professional with a strong foundation in traditional Data Science, including statistical modeling, Machine Learning (ML), and Deep Learning (DL), while also possessing hands-on experience with modern Generative AI and Large Language Model (LLM) solutions.

The ideal candidate will have excellent analytical and problem-solving skills, strong SQL expertise, and the ability to work independently with minimal supervision. This position is highly stakeholder-facing and requires someone who can translate business problems into scalable data-driven solutions.

Key Responsibilities

Design, develop, and implement advanced Data Science solutions leveraging statistical modeling, Machine Learning, and Deep Learning techniques.

Build and evaluate Generative AI and LLM-based solutions to address business challenges and improve operational efficiency.

Analyze large and complex datasets using SQL across Oracle (On-Prem), Google Cloud Platform, and AWS environments.

Work closely with business stakeholders to understand requirements, define objectives, and deliver actionable insights.

Develop predictive models, perform feature engineering, and validate model performance using industry best practices.

Collaborate with Data Engineering and MLOps teams to support model deployment and operationalization activities.

Stay current with emerging advancements in AI, Machine Learning, and Generative AI technologies.

Required Skills & Qualifications

Primary Skills

Strong foundation in Data Science, including:

Statistical Modeling

Machine Learning Algorithms

Deep Learning Techniques

Hands-on experience with Generative AI, Large Language Models (LLMs), and AI-driven solution development.

Strong SQL expertise with the ability to analyze and manipulate data in:

Oracle (On-Premises)

Google Cloud Platform (Google Cloud Platform)

Amazon Web Services (AWS)

Ability to independently solve complex business problems with minimal guidance.

Strong communication, presentation, and stakeholder management skills.

Secondary Skills

Understanding of MLOps concepts, workflows, and model lifecycle management.

Experience collaborating with MLOps teams for model deployment and monitoring activities.

Exposure to cloud-based data platforms and analytics ecosystems within Google Cloud Platform and AWS.